SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Showing 51100 of 15113 papers

TitleStatusHype
Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement LearningCode4
Cosmos-Reason1: From Physical Common Sense To Embodied ReasoningCode4
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLCode4
MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement LearningCode4
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement LearningCode4
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement LearningCode4
TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot ControlCode4
Diffusion Policy Policy OptimizationCode4
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningCode4
Pearl: A Production-ready Reinforcement Learning AgentCode4
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
TorchRL: A data-driven decision-making library for PyTorchCode4
Let's Verify Step by StepCode4
Mastering Diverse Domains through World ModelsCode4
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to RealityCode4
Discovering faster matrix multiplication algorithms with reinforcement learningCode4
RLlib Flow: Distributed Reinforcement Learning is a Dataflow ProblemCode4
RLlib: Abstractions for Distributed Reinforcement LearningCode4
Ray: A Distributed Framework for Emerging AI ApplicationsCode4
VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement LearningCode3
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPOCode3
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQLCode3
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement LearningCode3
General-Reasoner: Advancing LLM Reasoning Across All DomainsCode3
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement LearningCode3
Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement LearningCode3
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement LearningCode3
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement LearningCode3
Tina: Tiny Reasoning Models via LoRACode3
Learning to Reason under Off-Policy GuidanceCode3
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing ReasoningCode3
A Clean Slate for Offline Reinforcement LearningCode3
A Minimalist Approach to LLM Reasoning: from Rejection Sampling to ReinforceCode3
Perception-R1: Pioneering Perception Policy with Reinforcement LearningCode3
Multi-SWE-bench: A Multilingual Benchmark for Issue ResolvingCode3
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the MetaverseCode3
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn'tCode3
Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question AnsweringCode3
AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and ReasoningCode3
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsCode3
Demystifying Long Chain-of-Thought Reasoning in LLMsCode3
Flow Q-LearningCode3
Test-Time Training Scaling Laws for Chemical Exploration in Drug DesignCode3
SINERGYM -- A virtual testbed for building energy optimization with Reinforcement LearningCode3
Reinforcement Learning Enhanced LLMs: A SurveyCode3
o1-Coder: an o1 Replication for CodingCode3
OGBench: Benchmarking Offline Goal-Conditioned RLCode3
Streaming Deep Reinforcement Learning Finally WorksCode3
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character controlCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76Unverified
2PPOMean Normalized Performance0.58Unverified